Correlated Deep Q-learning based Microgrid Energy Management
This addresses energy management in microgrids for operators, but it is incremental as it builds on existing deep Q-learning methods.
The paper tackled microgrid energy management by proposing a correlated deep Q-learning technique to coordinate multiple electrical entities, resulting in 40.9% higher profit for the energy storage system agent and 9.62% for the photovoltaic agent.
Microgrid (MG) energy management is an important part of MG operation. Various entities are generally involved in the energy management of an MG, e.g., energy storage system (ESS), renewable energy resources (RER) and the load of users, and it is crucial to coordinate these entities. Considering the significant potential of machine learning techniques, this paper proposes a correlated deep Q-learning (CDQN) based technique for the MG energy management. Each electrical entity is modeled as an agent which has a neural network to predict its own Q-values, after which the correlated Q-equilibrium is used to coordinate the operation among agents. In this paper, the Long Short Term Memory networks (LSTM) based deep Q-learning algorithm is introduced and the correlated equilibrium is proposed to coordinate agents. The simulation result shows 40.9% and 9.62% higher profit for ESS agent and photovoltaic (PV) agent, respectively.